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Unformatted text preview: J. A. Harding e-mail: [email protected] M. Shahbaz e-mail: [email protected] Srinivas e-mail: [email protected] Wolfson School of Mechanical and Manufacturing Engineering, Loughborough University, Loughborough, Leicestershire LE2 4LA, UK A. Kusiak Department of Mechanical and Industrial Engineering, The University of Iowa, Iowa City, IA 52242-1527 e-mail: [email protected] Data Mining in Manufacturing: A Review The paper reviews applications of data mining in manufacturing engineering, in particu- lar production processes, operations, fault detection, maintenance, decision support, and product quality improvement. Customer relationship management, information integra- tion aspects, and standardization are also briefly discussed. This review is focused on demonstrating the relevancy of data mining to manufacturing industry, rather than dis- cussing the data mining domain in general. The volume of general data mining literature makes it difficult to gain a precise view of a target area such as manufacturing engineer- ing, which has its own particular needs and requirements for mining applications. This review reveals progressive applications in addition to existing gaps and less considered areas such as manufacturing planning and shop floor control. f DOI: 10.1115/1.2194554 g 1 Introduction Knowledge is the most valuable asset of a manufacturing en- terprise, as it enables a business to differentiate itself from com- petitors and to compete efficiently and effectively to the best of its ability. Knowledge exists in all business functions, including pur- chasing, marketing, design, production, maintenance and distribu- tion, but knowledge can be notoriously difficult to identify, cap- ture, and manage. Knowledge can be as simple as knowing who is best to contact if particular materials are running short, or can be as complex as mathematical formulas relating process variables to finished product dimensions. Spiegler f 1 g reviewed two models of knowledge. The first model follows a conventional hierarchy and transformation of data into information and knowledge with a spiral and recursive way of generating knowledge. The second model presents a reverse hierarchy where knowledge may appear before data and information processing. Knowledge discovery, knowledge management, and knowledge engineering are currently topics of importance to manufacturing researchers and managers intent on exploiting current assets. Database technology is central to all these knowledge-based research topics. The use of databases and statistical techniques are well estab- lished in engineering f 2 g . The first applications of artificial intel- ligence in engineering in general and in manufacturing in particu- lar were developed in the late 1980s f 3,4 g . The scope of these activities, however, has recently changed. The advancements in information technology s IT d , data acquisition systems, and storage technology as well as the developments in machine learning tools...
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- Spring '10
- Data Mining